Statistical learning is an established method of measuring implicit knowledge gained through observation. Rule learning employs a similar paradigm, but the knowledge gained is assumed to be more abstract and explicit. These two forms of learning have been considered separate mechanisms, and little is known about how their representations are stored in long-term memory or whether sleep provides a benefit for consolidation of such representations, as has been found in many implicit procedural learning tasks. The current study examines whether sleep benefits both statistical and implicit rule learning in a similar manner, and whether a short practice before test offers greater explicit insight into the underlying rules, as had been reported previously for abstract numerical rules. In our experiments, subjects first observed scenes of arbitrary shapes arranged as triplets repeated in random order for two minutes. The triplets contain a simple statistical structure, as particular triplets of shapes always appear together in fixed order, and two embedded rules: a size rule following an AAB pattern (small-small-large), and a color rule following ABA (dark-light-dark). After a twelve-hour delay, either overnight or over the day, subjects were tested on their knowledge of both the statistical structure and the size rule. A subset of subjects also completed a short “reminder” practice session before test. We found that the simple statistical structure was retained after twelve hours during the day but performance was improved by sleep (Day, M=64.6; Night, M=79.3). Rule knowledge was not retained, but emerged after sleep (Day, M=53.6; Night, M=61.8). However, a short practice session before test did not provide greater access to the implicitly learned rules (Day, M=54.7). These results indicate that sleep benefits the implicit knowledge gained during statistical and rule learning in a similar manner, but does not necessarily lead to improvement in discovery of explicit rules.